Time Domain Analysis Method of the Impulse Vibro-acoustic Signal for Fatigue Strength Characterisation of Metallic Material

Nuawi, Mohd. Zaki; Bahari, A. R.; Abdullah, Shahrum; Ariffin, A. K.; Nopiah, Z.M.

doi:10.1016/j.proeng.2013.12.106

Cited by 5 publications

(6 citation statements)

References 15 publications

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“…The combustion and piston motion noise have major contributions, hence it is necessary to analyze in more 8) can be decomposed into contributions due to combustion noise (1), contribution due to piston slap noise (2), contribution due to fan noise (3), contribution to gear operation noise (4), contribution due to pump operations (5), valve noise (6) and other sources (7) depths these aspects of engine noise and vibration acoustics. As evident from the above figures the combustion based noise and piston slapping noise have major contributing portions (about 80%), hence it is necessary to focus on these aspects of engine acoustics.…”

Section: Discussionmentioning

confidence: 99%

“…Hambric et al [3] studied ways to optimize structures to reduce vibrations. Nuawi et al [4] proposed statistical approach to characterize the fatigue strength of metallic material. Ji et al [5] reviewed modelling of extruded aluminum train floor structures to predict vibro-acoustic responses.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In order to obtain the levels which bears a closer relationship to loudness judgment than the sound pressure levels, three different networks of frequency weighting (A, B, and C) were incorporated into various sound level meters with the A-weighting most closely matches the human ear [3]. Due to higher compression ratios, diesel engines are known to produce higher noise emissions as compared to gasoline engines [4,5]. Higher compression ratio increases various forces on piston assembly at the ignition time that results in overall increase in vibrations of engine structure.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A review of vibro-acoustic techniques for control of combustion engine noise

Narayan¹,

Kaisan²,

Abubakar³

2021

J Appl Eng Science

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The present work focuses on various vibro-acoustic signals techniques that can monitor malfunctions in Internal Combustion Engines (ICEs). Recent works by other authors have focused on various reciprocating machines including compressors no attempts has been deal with IC engines. This paper gives a summary of the generation mechanism of sound and vibration in Engines. An overview of the monitoring and diagnostic techniques base on noise, pressure an vibration signatures is also discussed. Various fault conditions are described which affect ICEs. Measuring of acoustic signals has non-intrusive behavior with capability of detecting airborne transmission paths faults. In view of industrial needs to reduce the maintenance costs monitoring of vehicle operations, the present work can be a useful guide for engineers for understanding types of faults so as sufficient time is obtained to process reliable and information.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A review of vibro-acoustic techniques for control of combustion engine noise

Narayan¹,

Kaisan²,

Abubakar³

2021

J Appl Eng Science

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“…However, real vibration data from complex operating structures are often non-stationary, leading to less descriptive classical signal processing features as the data become decreasingly stationary. Impact and impulse measurements represent a major challenge for feature extraction as the dynamics are highly transient and short, in some cases causing subsequent values to differ in orders of magnitudes [4,6]. Hence, discriminative features cannot always be derived from classical methods like the Fourier transform for those signals.…”

Section: Introductionmentioning

confidence: 99%

Recovery of Differential Equations from Impulse Response Time Series Data for Model Identification and Feature Extraction

2019

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Time recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage of classical spectral signal processing techniques for (a) describing the dynamics and (b) deriving discriminative features from the data. However, common model identification and validation techniques mostly rely on steady-state recordings, characteristic spectral properties and non-transient behavior. In this work, a recent method, which allows reconstructing differential equations from time series data, is extended for higher degrees of automation. With special focus on short and strongly damped oscillations, an optimization procedure is proposed that fine-tunes the reconstructed dynamical models with respect to model simplicity and error reduction. This framework is analyzed with particular focus on the amount of information available to the reconstruction, noise contamination and nonlinearities contained in the time series input. Using the example of a mechanical oscillator, we illustrate how the optimized reconstruction method can be used to identify a suitable model and how to extract features from uni-variate and multivariate time series recordings in an engineering-compliant environment. Moreover, the determined minimal models allow for identifying the qualitative nature of the underlying dynamical systems as well as testing for the degree and strength of nonlinearity. The reconstructed differential equations would then be potentially available for classical numerical studies, such as bifurcation analysis. These results represent a physically interpretable enhancement of data-driven modeling approaches in structural dynamics.

show abstract

“…However, real vibration data from complex operating structures are often non-stationary, leading to less descriptive classical signal processing features as the data become decreasingly stationary. Impact and impulse measurements represent a major challenge for feature extraction as the dynamics are highly transient and short, in some cases causing subsequent values to differ in orders of magnitudes [5]. Hence, discriminative features cannot always be derived from classical methods like the Fourier transform for those signals.…”

Section: Introductionmentioning

confidence: 99%

Recovery of Differential Equations from Impulse Response Time Series Data for Model Identification and Feature Extraction

Stender¹,

Oberst²,

Hoffmann³

2018

Preprint

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Time recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage of classical spectral signal processing techniques for a) describing the dynamics and b) deriving discriminative features from the data. However, common model identification and validation techniques mostly rely on steady-state recordings, characteristic spectral properties and non-transient behavior. In this work, a recent method, which allows reconstructing differential equations from time series data, is extended for higher degrees of automation. With special focus on short and strongly damped oscillations, an optimization procedure is proposed that fine-tunes the reconstructed dynamical models with respect to model simplicity and error reduction. This framework is analyzed with particular focus on the amount of information available to the reconstruction, noise contamination and non-linearities contained in the time series input. Using the example of a mechanical oscillator, we illustrate how the optimized reconstruction method can be used to identify a suitable model and to extract features from uni-variate and multivariate time series recordings in an engineering-compliant environment. Moreover, the determined minimal models allow for identifying the qualitative nature of the underlying dynamical systems as well as testing for the degree and strength of non-linearity. The reconstructed differential equations would then be potentially available for classical numerical studies, such as bifurcation analysis. These results represent a physically interpretable enhancement of data-driven modeling approaches in structural dynamics.

show abstract

Time Domain Analysis Method of the Impulse Vibro-acoustic Signal for Fatigue Strength Characterisation of Metallic Material

Cited by 5 publications

References 15 publications

A review of vibro-acoustic techniques for control of combustion engine noise

A review of vibro-acoustic techniques for control of combustion engine noise

Recovery of Differential Equations from Impulse Response Time Series Data for Model Identification and Feature Extraction

Recovery of Differential Equations from Impulse Response Time Series Data for Model Identification and Feature Extraction

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